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Characterizing emerging European stock markets through complex networks: From local properties to self-similar characteristics

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  • Caraiani, Petre

Abstract

We investigate the properties of the returns of the main emerging stock markets from Europe by means of complex networks. We transform the series of daily returns into complex networks, and analyze the local properties of these networks with respect to degree distributions, clustering, or average line length. We further use the clustering coefficients as quantities describing the local structure of the network, and approach them by using multifractal analysis. We find evidence of scale-free networks and multifractality of clustering coefficients.

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File URL: http://www.sciencedirect.com/science/article/pii/S0378437112001240
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Bibliographic Info

Article provided by Elsevier in its journal Physica A: Statistical Mechanics and its Applications.

Volume (Year): 391 (2012)
Issue (Month): 13 ()
Pages: 3629-3637

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Handle: RePEc:eee:phsmap:v:391:y:2012:i:13:p:3629-3637

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Web page: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/

Related research

Keywords: Stock markets; Complex networks; Clustering; Determinism; Multifractality;

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  1. Bubák, Vít & Kocenda, Evzen & Zikes, Filip, 2011. "Volatility transmission in emerging European foreign exchange markets," Journal of Banking & Finance, Elsevier, vol. 35(11), pages 2829-2841, November.
  2. Domino, Krzysztof, 2011. "The use of the Hurst exponent to predict changes in trends on the Warsaw Stock Exchange," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(1), pages 98-109.
  3. G. Bonanno & G. Caldarelli & F. Lillo & S. Micciché & N. Vandewalle & R. Mantegna, 2004. "Networks of equities in financial markets," The European Physical Journal B - Condensed Matter and Complex Systems, Springer, vol. 38(2), pages 363-371, 03.
  4. Timotej Jagric & Boris Podobnik & Marko Kolanovic, 2005. "Does the Efficient Market Hypothesis Hold?: Evidence from Six Transition Economies," Eastern European Economics, M.E. Sharpe, Inc., vol. 43(4), pages 79-103, August.
  5. Kantelhardt, Jan W. & Zschiegner, Stephan A. & Koscielny-Bunde, Eva & Havlin, Shlomo & Bunde, Armin & Stanley, H.Eugene, 2002. "Multifractal detrended fluctuation analysis of nonstationary time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 316(1), pages 87-114.
  6. Chuang Liu & Wei-Xing Zhou, 2009. "Superfamily classification of nonstationary time series based on DFA scaling exponents," Papers 0912.2016, arXiv.org.
  7. Li, Ping & Wang, Bing-Hong, 2007. "Extracting hidden fluctuation patterns of Hang Seng stock index from network topologies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 378(2), pages 519-526.
  8. Qian, Xi-Yuan & Gu, Gao-Feng & Zhou, Wei-Xing, 2011. "Modified detrended fluctuation analysis based on empirical mode decomposition for the characterization of anti-persistent processes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(23), pages 4388-4395.
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